Figure 3 - available via license: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Content may be subject to copyright.
Sensitivity of the water temperature model to the input parameters. left Model outputs for all day simulation. right Model outputs during extraction hours (from 06.00 to 19:00).
Source publication
Domestic drinking water supply systems (DDWSs) are the final step in the delivery of drinking water to consumers. Temperature is one of the rate-controlling parameters for many chemical and microbiological processes and is, therefore, considered as a surrogate parameter for water quality processes. In this study, a mathematical model is presented t...
Citations
... Temperatures vary along water distribution processes on Earth due to weather, depth of pipes, and the use of water heaters in the dispensing process (Agudelo-Vera et al. 2020). Typically, drinking water temperatures at the tap (on cold) are less than 25 � C which falls in line with World Health Organization guidelines (World Health Organization 2011; Zlatanovic et al. 2017;Cincotta et al. 2024). Experiments investigating biofilm growth in water systems should likely take place near or just below 25 � C. Lower temperatures could be used, but microbial growth will likely be inhibited. ...
Biofilms are common in water systems and can lead to mechanical failure or illness of water system users. Methods for evaluating anti-fouling coatings have largely been informed by the medical industry and have not been tailored to industrial or spacecraft water systems. The goal of the paper is to help guide researchers in designing experiments to evaluate coatings that accurately represent the system under investigation. This review identified eight experimental design considerations when evaluating coatings in water systems: biofilm reactor operation, microorganism selection, reinoculation, coating surface area, liquid medium, experiment duration, coating performance evaluation, and the use of microgravity. The impact of each decision made within each of these considerations is presented. Further, the methods featured in eight studies investigating coatings for Earth-based or spacecraft water systems are discussed. This review serves to guide researchers toward improved experimental design to enable successful technology transfer from the lab bench to Earth and beyond.
... There have been a few studies attempting to model temperature in water supply systems [5] and urban drainage systems [6], with diverse approaches and assumptions. For premise plumbing systems in particular [7], recent studies have combined both hydraulics and temperature modelling proposing a one-dimensional radial approach which has had a satisfactory performance when tested against temperature measurements in real premise plumbing systems. ...
... Temperature-dependent growth and inactivation: Temperature (T) is simulated as in [8] using measurements at the service line for the cold system, while a constant 60 • C at the heater in the hot system is used, as required by Legionella regulations, since no temperature data were available from the heater outlet. Temperature-dependent µ is computed using a Gaussian curve according to Lp growth rates at 18-47 • C [3]. ...
... When temperature rises from 5 to 25°C, the level of viscosity will fall by approximately 40%, thus lowering flow resistance. It also has effects on the mobility of copper ions, the degree of deterioration, the dissolution of metal structures, the overall chlorinated breakdown, and the production of disinfection products [28,29]. Drinking water should have an ideal temperature of 25°C, as per the World Health Organization [30]. ...
This study was estimating the physicochemical parameters of water and preparing the water quality index for drinking water in a residential area of Bilaspur city. Fifty water samples were collected from ten sites and analyzed six parameters of water quality by using the portable multi-parameter water quality meter (Hanna Instruments: HI98194). The results of water quality were statistically different for sites (p<0.001). During the study, the average water pH (8.326±0.67), water temperature (27.349±0.207 °C), dissolved oxygen (7.775±0.034 mg/l), total dissolved solids (526.46±0.781mg/l), electrical conductivity of water (391.6±0.79 mg/l), and oxygen reduction potential (-32.715±0.21 mV) were recorded. The positive correlation was observed between EC and TDS (r = 0.935) and pH and ORP (r = 0.802), while the negative correlation was observed between DO and temperature. The range of the WQI was observed to be 383.67 to 530.87, and there was a statistically difference at for sites (p<0.001).
... However, it is suggested that concentrations of inorganics less than 0.3 mg/l have insignificant effect on residual chlorine decay [24]. Temperature is another key water quality parameter that influences residual chlorine decay [2,4,8,9,12,13,15,16,[18][19][20]25,26]. Temperature varies spatially and temporally with residual chlorine decay in water distribution networks [23]. ...
... Consequently, there was insignificant and negligible correlation between velocity and residual chlorine decay. Temperature as measure of thermal energy influences chemical reactions [18,26] and affects all water quality processes [25,26]. However, there was little variation in temperature over the study area and during study time. ...
... Consequently, there was insignificant and negligible correlation between velocity and residual chlorine decay. Temperature as measure of thermal energy influences chemical reactions [18,26] and affects all water quality processes [25,26]. However, there was little variation in temperature over the study area and during study time. ...
Chlorine is the most common disinfectant in drinking water distribution practice. World Health Organization recommends 0.2–5.0 mg/l of residual chlorine in drinking water. This paper analyzed influence of physical and water quality parameters on chlorine decay in drinking water distribution. Principal component analysis, directed tree and regression were used to investigate influence of these parameters on chlorine from water treatment plant to water consumption points. Results show that initial chlorine, electrical conductivity and distance explain 62 % of chlorine decay with estimated error of 0.045 mg/l. The decision-tree feature importance scores of initial chlorine and electrical conductivity were 0.47 and 0.23 respectively. The combined feature importance scores of physical parameters of distance (0.09), pipe diameter (0.06), flow velocity (0.03), pressure (0.02) and travel time (0.046) were less than that for initial chlorine concentration (0.47) alone. These results show that conventional chlorination at water treatment plants removes largely fast inorganic reactants leaving traces of slow organic reactants as the dominant secondary contaminants in water distribution system. The key policy recommendation is to use water quality parameters more than physical parameters in order to enable water utility managers maintain residual chlorine within safe public health standards.
... Premise plumbing layout used in this study. 52 The lines represent pipes, and the dots represent pipe junctions or fixtures. The hot and cold water pipes are mapped separately, with the hot water heater pictured with an upward arrow with a horizontal line above it. ...
... The 4 L cold water purge at the shower draws water directly from the distribution system. a typical Dutch terraced house from Zlatanovic et al., 52 which included a washing machine; a full bathroom with a shower, sink, and toilet; a half bathroom with a sink and toilet; and a kitchen with a sink and dishwasher. The node elevations, reservoir head, and diameters and lengths of the pipes used by Clements et al. 23 are reproduced in the Supporting Information (Tables S1−S3). ...
Water age in drinking water systems is often used as a proxy for water quality but is rarely used as a direct input in assessing microbial risk. This study directly linked water ages in a premise plumbing system to concentrations of Legionella pneumophila via a growth model. In turn, the L. pneumophila concentrations were used for a quantitative microbial risk assessment to calculate the associated probabilities of infection (Pinf) and clinically severe illness (Pcsi) due to showering. Risk reductions achieved by purging devices, which reduce water age, were also quantified. The median annual Pinf exceeded the commonly used 1 in 10,000 (10–4) risk benchmark in all scenarios, but the median annual Pcsi was always 1–3 orders of magnitude below 10–4. The median annual Pcsi was lower in homes with two occupants (4.7 × 10–7) than with one occupant (7.5 × 10–7) due to more frequent use of water fixtures, which reduced water ages. The median annual Pcsi for homes with one occupant was reduced by 39–43% with scheduled purging 1–2 times per day. Smart purging devices, which purge only after a certain period of nonuse, maintained these lower annual Pcsi values while reducing additional water consumption by 45–62%.
... The sign of each loading factor aligns with expectations about how water age affects each variable. For example, literature suggests that with increasing water age, HPC concentrations, and water temperature will increase while concentrations of residual chlorine and DO will decline (AWWA, 2002;Pierre et al., 2019;Rhoads et al., 2016;Zlatanovic et al., 2017;Zlatanovi c et al., 2017). The water metrics vol.events and meanTSL are negatively and positively related to PC1, respectively, further associating PC1 with water age. ...
The residence time of water in residential building water systems is a critical factor regarding water quality at end use. Published literature has highlighted the importance of water age in these systems and its relationship with pathogenic bacteria such as Legionella pneumophila . However, tools to measure water age in such plumbing systems are typically repurposed from other applications and include limitations that make them inappropriate for some plumbing systems. This work presents a novel means of estimating water age by assuming these systems operate without mixing. Data for this study was collected from a full‐scale home equipped with an extensive array of flow meters to monitor water use. Further, 408 individual water quality samples were collected to ascertain water quality changes that take place in the plumbing. Model results show weak correlation with EPANET 2.2 ( ρ = 0.666), a commonly used hydraulic modeling software. The results of the water age model were also evaluated with several variable selection tools. These analyses indicate that this method's water age results are a statistically significant ( p < .05) predictor of Legionella concentrations. Model results from this approach could be used in plumbing design and/or operation to assist in managing Legionella risks.
... The analyses in this study were based on a plumbing configuration from a previous study on a typical Dutch terraced house. 34 The supply pipe from the distribution system was much shorter than typically found for homes in the United States, and the home had a tankless water heater. The house consisted of three stories, with a washing machine on the third floor, a bathroom with a toilet, shower, and sink on the second floor, and a kitchen with a tap and dishwater, and a bathroom with a toilet and tap on the first floor (Fig. 1). ...
... The house consisted of three stories, with a washing machine on the third floor, a bathroom with a toilet, shower, and sink on the second floor, and a kitchen with a tap and dishwater, and a bathroom with a toilet and tap on the first floor (Fig. 1). 34 Information about node location and elevation (Table S1 †), reservoir ( 35 are good for analyzing measured flows, SIMDEUM is a predictive, Monte Carlo model, accommodating seamlessly a statistical, stochastic analysis. By producing demands for each household occupant at each fixture, SIMDEUM allows for study of water use changes when the occupants or fixture types change at a household level. ...
... There is no mixing of mass between adjacent water parcels in the model, though mixing at junctions is complete and instantaneous. 42 To track the parcels of water 34 The lines correspond to the pipes of the system and the dots correspond to pipes junctions or fixtures. The water enters from the distribution system, pictured as a reservoir, with a water age of 0 hours. ...
Water age is often used as a proxy for water quality in drinking water systems, but the highly stochastic nature of water demands in premise plumbing systems, and nonlinear relationship...
... The research presented in [18] shows that different biofilm compositions can exist within one DDWS, which is influenced by the environmental conditions inside the installation, the microclimate in the house, and the frequency of water consumption. In the studies carried out by Candida, a higher number of biofilm microorganisms was obtained, their number was on average 1.0 × 10 7 bacteria/cm 2 (the plateau was reached on days 2-3) [32]. ...
... The temperature of the water supplying the experimental installation was on average 22 • C. Obtaining such a high temperature is the result of the thermal equilibrium between the water filling the installation and the air in the laboratory. The rate of increasing the tap water temperature to room temperature may be about 0.1 • C/minute [18,52]. According to the existing standards, the air temperature in rooms should be 22 • C in winter and 24.5 • C in summer [53]. ...
In the conducted study, an attempt was made to verify and evaluate the impact of the biofilm formed on the surfaces of the installation material on the quality and sanitary safety of tap water reaching the consumer. For biofilm studies, fractal analysis and quantitative bacteriological analysis were used. The quality of tap water flowing through the experimental installation (semi-technical scale) was determined using physicochemical and microbiological parameters. The quantitative analysis of the biofilm showed that an increase in the number of microorganisms was observed in the initial phase of biofilm formation (reached 1.4 × 104 CFU/ml/cm2 on day 14). During this period, there was a chaotic build-up of bacterial cells, as evidenced by an increase in the roughness of the profile lines. Unstable elevations of the biofilm formed in this way could be easily detached from the structure of the material, which resulted in deterioration of the bacteriological quality of the water leaving the installation. The obtained results indicate that the biofilm completely and permanently covered the surface of the tested material after 25 days of testing (the surface roughness described by the fractal dimension decreased). Moreover, the favorable temperature (22.6 °C) and the recorded decrease in the content of inorganic nitrogen (by 15%), phosphorus (by 14%), and dissolved oxygen (by 15%) confirm the activity of microorganisms. The favorable environmental conditions in the installation (the presence of nutrients, low chlorine concentration, and high temperature) contributed to the secondary development of microorganisms, including pathogenic organisms in the tested waters.
... These parameters are to an extent, influenced by changes to water temperature. Increasing temperature will reduce the absorption rate of oxygen and other gases [4]. These affect photosynthesis by aquatic plants and thus, resulting in lower DO [5]. ...
p>Water quality plays a major role in issues related to public health and marine life. Hence, monitoring river for contaminations is vital for ensuring safe and sustainable water resources. Conventional method for assessing water quality index is costly as it requires considerable amount of time and laboratory resources. Therefore, this study proposes a water quality index model based on artificial neural network. A six-year data for Air Busuk River is obtained from the Department of Environment. Dissolved oxygen, biological oxygen demand, and ammoniacal nitrogen has shown high correlation with water quality index. The water quality index model is then developed based on these parameters, employing the non-linear autoregressive with exogeneous input structure. Generally, the model which is based on three chemical parameters has shown satisfactory performance with overall regression of 0.8767 and passed the correlation function tests. The model offers a potentially efficient method for assessing water quality with cost-saving benefits for government agencies and monitoring authorities.</p
